Flight Fare Prediction

Abstract

The Flight Fare Prediction Web App is a data-driven solution designed to estimate airline ticket prices with precision by leveraging historical fare data and advanced machine learning algorithms. By incorporating multiple flight-related attributes, the system enhances predictive accuracy, factoring in elements such as airline name, departure and arrival times, flight duration, number of stops, travel date, source and destination airports, travel class, and fare history trends. Recognizing the influence of airline branding, peak travel hours, and seasonal demand fluctuations, the model categorizes flights based on time slots, seating class, and route-specific pricing patterns. Longer flights, additional layovers, and high-demand routes typically result in dynamic pricing variations. Through data preprocessing, feature engineering, and model training, the system implements machine learning techniques such as Random Forest and XGBoost, optimizing performance through hyperparameter tuning and validating accuracy using Mean Absolute Error (MAE). The web application, developed using Flask, provides an intuitive interface where users can input flight details and obtain real-time fare predictions, aiding in cost-efficient travel planning. The backend, built with Python and utilizing CSV◻based storage, ensures scalability and flexibility without requiring complex databases. This predictive tool benefits both travelers seeking budget-friendly options and airlines aiming to refine their pricing strategies through data-driven insights